GAN loss.
We combine a conditional GAN [1]} and a Least Squares GAN [2]} for the gl:DSM refinement network, thus a gl:cLSGAN loss is utilized:
\(\begin{array}{c}\underset{G}{\min } \underset{D}{\max } \mathcal {L}_{\text{cLSGAN }}(G, D)=\mathbb {E}_{x, y \sim p_{\text{real }}(y)}\left[(D(y,x)-1)^{2}\right] \\\end{array}+\mathbb {E}_{x, z \sim p_{z}(z)}\left[D(G(z,x),x)^{2}\right]\) 
